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Pipeworx Trending

pipeworx_trending
Read-onlyIdempotent

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds valuable context: data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise paragraphs. First sentence defines purpose. Then bullet-like use cases, followed by technical details. No unnecessary words, front-loaded with action and resource.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given one optional parameter and no output schema, the description explains return values (top tools, packs, volume) and caching. Sufficient for agent to understand and use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage with enum description. Description adds semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' Adds value beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns top tools, top packs, and total call volume over a window. It provides three specific use cases and distinguishes itself from sibling tools as a trending data source.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly lists three use cases (discovering hot data sources, confirming canonical tools, seeing alignment). Could be improved by mentioning when not to use it, but context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation2/5

Several tools have nearly identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, polymarket_arbitrage and polymarket_edges both scan for betting opportunities, and ai_visibility_check overlaps with scan_competitor_ai_presence. The long descriptions help, but an agent must read carefully to distinguish the meta-routers (ask_pipeworx, deep_research, discover_tools, suggest_questions) from each other.

Naming Consistency4/5

The overwhelming majority use lowercase snake_case with a verb-first or domain-prefixed noun pattern (ask_pipeworx, sec_regulations_search, polymarket_edges). Minor deviations like entity_profile, recent_alerts, and ai_visibility_check are noun-first, and sec_regulation vs sec_regulations_search is slightly inconsistent, but the overall style is predictable.

Tool Count2/5

At 33 tools this exceeds the 25-tool threshold for 'too many', and most are unrelated to the server's stated name 'Sec Regulations'—only sec_regulation and sec_regulations_search fit that label. While each tool has a defined purpose, the set feels like several distinct servers (research, betting, memory, subscriptions) crammed into one.

Completeness3/5

For a general data-research platform, the surface is broad: Q&A routers, entity resolution/profiles, comparisons, validation, memory, subscriptions, and domain-specific tools. But the SEC-regulation focus implied by the server name is severely under-served, and there's no direct tool to fetch a raw document by citation/URI outside of the ask_pipeworx router. The Polymarket cluster is over-built relative to other data domains.